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ARXIV:2603.24704 · AI RISK MANAGEMENT · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.24704AI RISK MANAGEMENTSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALETian Bai · Ying Jin · arXiv
A framework for AI models to precisely control prediction risk, ensuring reliability in critical applications like drug discovery and healthcare.
Opportunity summary
Pain A framework for AI models to precisely control prediction risk, ensuring reliability in critical applications like drug discovery and healthcare.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A framework for AI models to precisely control prediction risk, ensuring reliability in critical applications like drug discovery and healthcare. To fulfill its promise, it is crucial to enforce strict and precise error control…
In deploying artificial intelligence (AI) models, selective prediction offers the option to abstain from making a prediction when uncertain about model quality. To fulfill its promise, it is crucial to enforce strict and precise…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We evaluate the proposed methods with simulations and demonstrate their efficacy through applications to error management in drug discovery, health risk prediction, and large…
AI Risk Management moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A framework for AI models to precisely control prediction risk, ensuring reliability in critical applications like drug discovery and healthcare.
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Paper Pack
10.48550/arXiv.2603.24704A framework for AI models to precisely control prediction risk, ensuring reliability in critical applications like drug discovery and healthcare.
Abstract
In deploying artificial intelligence (AI) models, selective prediction offers the option to abstain from making a prediction when uncertain about model quality. To fulfill its promise, it is crucial to enforce strict and precise error control over cases where the model is trusted. We propose Selective Conformal Risk control with E-values (SCoRE), a new framework for deriving such decisions for any trained model and any user-defined, bounded and continuously-valued risk. SCoRE offers two types of guarantees on the risk among ``positive'' cases in which the system opts to trust the model. Built upon conformal inference and hypothesis testing ideas, SCoRE first constructs a class of (generalized) e-values, which are non-negative random variables whose product with the unknown risk has expectation no greater than one. Such a property is ensured by data exchangeability without requiring any modeling assumptions. Passing these e-values on to hypothesis testing procedures, we yield the binary trust decisions with finite-sample error control. SCoRE avoids the need of uniform concentration, and can be readily extended to settings with distribution shifts. We evaluate the proposed methods with simulations and demonstrate their efficacy through applications to error management in drug discovery, health risk prediction, and large language models.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
A framework for AI models to precisely control prediction risk, ensuring reliability in critical applications like drug discovery and healthcare. To fulfill its promise, it is crucial to enforce strict and precise error control over cases where the model is trusted.
METHOD
In deploying artificial intelligence (AI) models, selective prediction offers the option to abstain from making a prediction when uncertain about model quality. To fulfill its promise, it is crucial to enforce strict and precise error control over cases where the model is truste...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We evaluate the proposed methods with simulations and demonstrate their efficacy through applications to error management in drug discovery, health risk prediction, and large language models. Code availab...
WHY NOW
AI Risk Management moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework for AI models to precisely control prediction risk, ensuring reliability in critical applications like drug discovery and healthcare. To fulfill its promise, it is crucial to enforce strict and precise error control over cases where the model is trusted.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
In deploying artificial intelligence (AI) models, selective prediction offers the option to abstain from making a prediction when uncertain about model quality. To fulfill its promise, it is crucial to enforce strict and precise error control over cases where the model is trusted.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We evaluate the proposed methods with simulations and demonstrate their efficacy through applications to error management in drug discovery, health risk prediction, and large language models. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Risk Management moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
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A framework for AI models to precisely control prediction risk, ensuring reliability in critical applications like drug discovery and healthcare.
Segment
AI Risk Management
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
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CITED BY
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
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Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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People
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Gaps
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
Buzz trend pending.